TopoTxR: A Topological Biomarker for Predicting Treatment Response in Breast Cancer
TopoTxR: A Topological Biomarker for Predicting Treatment Response in Breast Cancer
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DOI：
10.1007/978-3-030-78191-0_30
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发表时间：
2021-05
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影响因子：
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通讯作者：
Fan Wang;S. Kapse;Steven Liu;P. Prasanna;Chao Chen
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作者：
Fan Wang;S. Kapse;Steven Liu;P. Prasanna;Chao Chen
动态对比增强磁共振成像（DCE-MRI）乳腺实质的表征是一项具有挑战性的任务，由于底层组织结构的复杂性。目前的定量方法，包括放射组学和深度学习模型，并没有明确地捕捉复杂和微妙的实质结构，如纤维腺组织。在本文中，我们提出了一种新的方法来指导神经网络的注意力周围的生物相关的组织结构的一组专用的体素。通过提取具有高显着性的多维拓扑结构，我们建立了一个拓扑衍生的生物标志物，TopoTxR。我们证明TopoTxR在预测乳腺癌新辅助化疗反应中的有效性。我们的定性和定量结果表明，在对治疗反应良好的患者与对治疗反应不佳的患者中，乳腺组织在未经治疗的成像上的拓扑行为不同。
Characterization of breast parenchyma on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity of underlying tissue structures. Current quantitative approaches, including radiomics and deep learning models, do not explicitly capture the complex and subtle parenchymal structures, such as fibroglandular tissue. In this paper, we propose a novel method to direct a neural network’s attention to a dedicated set of voxels surrounding biologically relevant tissue structures. By extracting multi-dimensional topological structures with high saliency, we build a topology-derived biomarker,TopoTxR. We demonstrate the efficacy ofTopoTxRin predicting response to neoadjuvant chemotherapy in breast cancer. Our qualitative and quantitative results suggest differential topological behavior of breast tissue on treatment-naïve imaging, in patients who respond favorably to therapy versus those who do not.